README.Rmd

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output: github_document
---

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The goal of *coconat* is to provide dataset agnostic support for connectome analysis.

It is intended to have a mix of end-user functionality and functions that are
principally intended as building blocks for more specialised packages such as
https://natverse.org/neuprintr/ or https://natverse.org/fafbseg/.

It is also a key building block for the https://natverse.org/coconat package,
which provides a small number of powerful functions for comparative connectomics
analysis of multiple dense reconstructions of drosophila brain and VNC data.

## Installation

You can install the development version of coconat from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("natverse/coconat")
```

## Example

This is a basic example which shows you how to solve a common problem:

```{r example}
library(coconat)
# da2ds=neuprintr::neuprint_connection_table('DA2_lPN', details=TRUE, partners='out', conn = hbconn)
da2ds=readRDS(system.file('sampledata/da2ds.rds', package = 'coconat'))
head(da2ds)
```

```{r}
library(Matrix)
am=partner_summary2adjacency_matrix(da2ds, inputcol = 'bodyid', outputcol = 'partner')
image(am)
```

```{r}

am2=partner_summary2adjacency_matrix(subset(da2ds, weight>=15), 
                                     inputcol = 'bodyid', outputcol = 'partner')
image(am2)
```
Now we can also cosine cluster


We can do this for the inputs (the 5 PNs should look much the same)
```{r}
heatmap(cosine_sim(am, transpose = T))
```

Or the outputs

```{r}
heatmap(cosine_sim(am))
```

But it would of course be more useful if we did this based on a richer set of inputs